import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__  =					logging.get_logger(__name__)


def 				lowerCAmelCase__       (			a__    ,     a__=False    ,     a__=False )      ->List[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    "backbone." if is_semantic else ""

  _UpperCamelCase           =    []
  for i in range(config.num_hidden_layers ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight') )
    rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias') )
    rename_keys.append(
        (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight') )
    rename_keys.append(
        (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias') )
    rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight') )
    rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias') )
    rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight') )
    rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias') )
    rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight') )
    rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias') )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (f'{prefix}cls_token', "beit.embeddings.cls_token"),
          (f'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
          (f'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
          (f'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
      ] )

  if has_lm_head:
    # mask token + layernorm
    rename_keys.extend(
        [
            ("mask_token", "beit.embeddings.mask_token"),
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
        ] )
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("fc_norm.weight", "beit.pooler.layernorm.weight"),
            ("fc_norm.bias", "beit.pooler.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ] )

  return rename_keys


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=False    ,     a__=False )      ->List[Any]:




  '''simple docstring'''

  for i in range(config.num_hidden_layers ):
    _UpperCamelCase           =    "backbone." if is_semantic else ""
    # queries, keys and values
    _UpperCamelCase           =    state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight' )
    _UpperCamelCase           =    state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias' )
    _UpperCamelCase           =    state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias' )

    _UpperCamelCase           =    in_proj_weight[
        : config.hidden_size, :
    ]
    _UpperCamelCase           =    q_bias
    _UpperCamelCase           =    in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _UpperCamelCase           =    in_proj_weight[
        -config.hidden_size :, :
    ]
    _UpperCamelCase           =    v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    _UpperCamelCase           =    state_dict.pop(f'{prefix}blocks.{i}.gamma_1' )
    _UpperCamelCase           =    state_dict.pop(f'{prefix}blocks.{i}.gamma_2' )

    _UpperCamelCase           =    gamma_a
    _UpperCamelCase           =    gamma_a


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    dct.pop(a__ )
  _UpperCamelCase           =    val


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
  _UpperCamelCase           =    Image.open(requests.get(a__    ,     stream=a__ ).raw )
  return im


@torch.no_grad()
def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=False )      ->Union[str, Any]:




  '''simple docstring'''

  _UpperCamelCase           =    False if "rvlcdip" in checkpoint_url else True
  _UpperCamelCase           =    BeitConfig(use_absolute_position_embeddings=a__    ,     use_mask_token=a__ )

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    _UpperCamelCase           =    1_024
    _UpperCamelCase           =    4_096
    _UpperCamelCase           =    24
    _UpperCamelCase           =    16

  # labels
  if "rvlcdip" in checkpoint_url:
    _UpperCamelCase           =    16
    _UpperCamelCase           =    "huggingface/label-files"
    _UpperCamelCase           =    "rvlcdip-id2label.json"
    _UpperCamelCase           =    json.load(open(hf_hub_download(a__    ,     a__    ,     repo_type="dataset" )    ,     "r" ) )
    _UpperCamelCase           =    {int(a__ ): v for k, v in idalabel.items()}
    _UpperCamelCase           =    idalabel
    _UpperCamelCase           =    {v: k for k, v in idalabel.items()}

  # load state_dict of original model, remove and rename some keys
  _UpperCamelCase           =    torch.hub.load_state_dict_from_url(a__    ,     map_location="cpu" )["model"]

  _UpperCamelCase           =    create_rename_keys(a__    ,     has_lm_head=a__ )
  for src, dest in rename_keys:
    rename_key(a__    ,     a__    ,     a__ )
  read_in_q_k_v(a__    ,     a__    ,     has_lm_head=a__ )

  # load HuggingFace model
  _UpperCamelCase           =    BeitForMaskedImageModeling(a__ ) if has_lm_head else BeitForImageClassification(a__ )
  model.eval()
  model.load_state_dict(a__ )

  # Check outputs on an image
  _UpperCamelCase           =    BeitImageProcessor(
      size=config.image_size    ,     resample=PILImageResampling.BILINEAR    ,     do_center_crop=a__ )
  _UpperCamelCase           =    prepare_img()

  _UpperCamelCase           =    image_processor(images=a__    ,     return_tensors="pt" )
  _UpperCamelCase           =    encoding["pixel_values"]

  _UpperCamelCase           =    model(a__ )
  _UpperCamelCase           =    outputs.logits

  # verify logits
  _UpperCamelCase           =    [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
  assert logits.shape == torch.Size(a__ ), "Shape of logits not as expected"

  Path(a__ ).mkdir(exist_ok=a__ )
  print(f'Saving model to {pytorch_dump_folder_path}' )
  model.save_pretrained(a__ )
  print(f'Saving image processor to {pytorch_dump_folder_path}' )
  image_processor.save_pretrained(a__ )

  if push_to_hub:
    if has_lm_head:
      _UpperCamelCase           =    "dit-base" if "base" in checkpoint_url else "dit-large"
    else:
      _UpperCamelCase           =    "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
    image_processor.push_to_hub(
        repo_path_or_name=Path(a__    ,     a__ )    ,     organization="nielsr"    ,     commit_message="Add image processor"    ,     use_temp_dir=a__    ,     )
    model.push_to_hub(
        repo_path_or_name=Path(a__    ,     a__ )    ,     organization="nielsr"    ,     commit_message="Add model"    ,     use_temp_dir=a__    ,     )


if __name__ == "__main__":
   lowerCamelCase__  =					argparse.ArgumentParser()

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
       type=str,
       help='''URL to the original PyTorch checkpoint (.pth file).''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
   )
   lowerCamelCase__  =					parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()




